Fault diagnosis prediction system and method of large-span railroad bridge based on PHM

Abstract

The invention discloses a fault diagnosis prediction system of a large-span railroad bridge based on PHM. The system comprises a visualization management module, an archival data module, an online monitoring module, an artificial polling module, a diagnosis prediction module, a maintenance module, a data interaction module and a PHM database. The invention further provides a fault diagnosis prediction method of the large-span railroad bridge based on the PHM. The fault diagnosis prediction system has the advantages that 3S network architecture, a BIM and a GIS technology are adopted to associate and archive design, construction, operation and maintenance information, by comprehensively monitoring trains, tracks, bridges, and bridge environments and processing big data of artificial pollinginformation, the diagnosis and prediction of bridge diseases and evaluation of health states of the bridge are achieved, and a decision-making basis is provided for the maintenance of the bridges.

Description

technical field [0001] The invention relates to the technical field of railway bridges, in particular to a PHM-based fault diagnosis and prediction system and method for long-span railway bridges. Background technique [0002] The long-span railway bridge is the control key project of the railway line. With the prolongation of service time, under the influence of load and environmental factors, various damages and diseases of these bridges will inevitably occur. On the one hand, these diseases affect the durability of the structure and shorten the service life of the bridge structure; on the other hand, they can reduce the strength and rigidity of the structure, burying safety hazards for railway operations. [0003] At present, the diseases of railway bridges are mainly found through manual periodic inspections. However, a large number of manual inspection logs have not been digitized and informatized in a timely manner, and the description of the location of the disease ...

AI Technical Summary

Problems solved by technology

However, on the one hand, the bridge health monitoring system only has a small number of sensors installed on the key parts or key components of the main structure, so it is impossible to effectively monitor all parts or all diseases of the bridge in real time; on the other hand, massive Bridge health monitoring data is difficult to directly guide the maintenance of bridges, and the correlation degree of various monitoring data is very low, which cannot effectively serve the management and maintenance of bridges

More importantly, the existing health monitoring system only monitors the condition of the main structure of the bridge and its environment, and it is difficult to directly evaluate the safety status of trains operating on the bridge track

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